Linear and Polynomial Regression in MATLAB

This tutorial covers fitting lines and polynomials to data using MATLAB. It explains how to calculate the intercept, slope, and R squared value, and demonstrates fitting quadratic and cubic functions. The tutorial includes using MATLAB commands for data regression, plotting data points, and adjusting plot appearance. It concludes with calculating R squared for linear regression.

What is the purpose of calculating the intercept and slope in data fitting?

• To find the best-fit line for the data

• To determine the curvature of the data

• To identify outliers in the data

• To calculate the average of the data points

Which function is used in MATLAB for polynomial fitting?

• linspace

• fitline

• polyfit

• plot

What is the purpose of using the 'linspace' function in MATLAB?

• To create a logarithmic space

• To generate a linearly spaced vector

• To fit a polynomial to data

• To clear variables in the workspace

How can you make a plot smoother in MATLAB?

• By decreasing the number of data points

• By using a thicker line width

• By changing the plot color

• By increasing the number of data points using 'linspace'

What does the R squared value indicate in regression analysis?

• The number of data points

• The intercept of the regression line

• The goodness of fit of the model

• The slope of the regression line

What is the residual in the context of regression analysis?

• The slope of the regression line

• The sum of all data points

• The difference between the observed and predicted values

• The average of the data points

How is the sum of squared errors used in regression analysis?

• To calculate the slope of the regression line

• To find the maximum value in the dataset

• To determine the goodness of fit

• To calculate the average of the data points